DARWIN Alzheimer’s All Options

1 DARWIN Univariate Options Analysis

1.0.1 Loading the libraries

library("FRESA.CAD")
library(readxl)
library("whitening")
library("fpc")
library("robust")
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
TrainFraction <- 0.50;

dataLoad = FALSE

1.1 The Data

DARWIN <- read.csv("~/GitHub/FCA/Data/DARWIN/DARWIN.csv")
rownames(DARWIN) <- DARWIN$ID
DARWIN$ID <- NULL
DARWIN$class <- 1*(DARWIN$class=="P")
print(table(DARWIN$class))
#> 
#>  0  1 
#> 85 89

DARWIN[,1:ncol(DARWIN)] <- sapply(DARWIN,as.numeric)


set.seed(1)
trainCases <- sample(nrow(DARWIN),nrow(DARWIN)*TrainFraction)

DARWIN_train <- DARWIN[trainCases,]
DARWIN_test <- DARWIN[-trainCases,]

pander::pander(table(DARWIN_train$class))

0	1
48	39

pander::pander(table(DARWIN_test$class))

0	1
37	50

1.1.0.1 Analysis parameters

dataframe <- DARWIN_train
dataframeTest <- DARWIN_test
outcome <- "class"

1.1.1 All the run options

sig_pvalue <- 0.05
thr <- c(0.05,0.2,0.4,0.6,0.8,0.95);
method  <- c("fast","pearson","spearman");
#method  <- c("fast")
type <- c("LM","RLM")
#type <- c("LM")
DeOutcome <- c("T_Blind","T_Driven")
corRank <- c(FALSE,TRUE)

#method  <- c("fast","pearson","spearman");
#type <- c("LM","RLM")

1.2 Decorrelation Analysis

if (dataLoad)
{
  load("~/GitHub/LatentBiomarkers/RMD/DARWIN_ALL_Options.RData")
} else
{
  
  idx = 0;
  thenames <- list();
  totBaM <- NULL
  totDeM <- NULL
  toUnmatM <- NULL
  unalteredM <- NULL
  Decorrleated_FractionM<- NULL
  Base_FractionM<- NULL
  Unaltered_FractionM <- NULL
  sparcityM <- NULL
  Average_Latent_SizeM <- NULL
  SigDeM <- NULL
  La_SignificantM <- NULL
  pbKNNaucM <- NULL
  pbKNNaccM <- NULL
  
  for (DeOut in DeOutcome)
  {
    for (meth in method)
    {
      for (typ in type)
      {
        for (corran in corRank)
        {
          par(op)
          par(mfrow=c(3,2),cex=0.5)
          idx <- idx + 1;
          thenames[[idx]] <- paste(DeOut,meth,typ,corran,sep="_")
          totBa <- numeric()
          totDe <- numeric()
          toUnmat <- numeric()
          unaltered <- numeric()
          Decorrleated_Fraction<- numeric()
          Base_Fraction<- numeric()
          Unaltered_Fraction <- numeric()
          sparcity <- numeric()
          Average_Latent_Size  <- numeric()
          SigDe <- numeric()
          La_Significant <- numeric()
          pbKNNauc <- numeric()
          pbKNNacc <- numeric()
          for (thrs in thr)
          {
            print(thenames[[idx]])
            plotname <- paste(thenames[[idx]],thrs,sep="_")
            
  
            if (DeOut != "T_Driven")
            {
              DEdataframeTrain <- IDeA(dataframe,
                                                     thr=thrs,
                                                     method=meth,
                                                     type=typ,
                                                     corRank=corran,
                                                     relaxed=TRUE,
                                                     verbose = FALSE)
            } else
            {
              DEdataframeTrain <- IDeA(dataframe,
                                                     Outcome=outcome,
                                                     thr=thrs,
                                                     method=meth,
                                                     type=typ,
                                                     corRank=corran,
                                                     relaxed=TRUE,
                                                     verbose = FALSE)
            }
            demat <- attr(DEdataframeTrain,"UPSTM")
            DEdataframe <- predictDecorrelate(DEdataframeTrain,dataframeTest)
            totFe <- ncol(DEdataframe)-1
            totBa <- c(totBa,length(attr(DEdataframeTrain,"unaltered")))
            totDe <- c(totDe, sum(str_detect(colnames(DEdataframe),"La_")))
            toUn <- sum(apply(demat!=0,2,sum)==1)
            toUnmat <- c(toUnmat,toUn )
            una <-  totFe - ncol(demat) + toUn
            unaltered <- c(unaltered,una)
            Decorrleated_Fraction <- c(Decorrleated_Fraction,sum(str_detect(colnames(DEdataframe),"La_"))/totFe)
            Base_Fraction <- c(Base_Fraction,length(attr(DEdataframeTrain,"unaltered"))/totFe)
            
            Unaltered_Fraction <- c(Unaltered_Fraction,una/totFe)
            sparcity <- c(sparcity,(totFe-ncol(demat)+sum(abs(demat)!=0))/totFe/totFe)
            
            varlistDe <-  colnames(demat)[apply(demat!=0,2,sum)>1];
            varlistDe <- as.data.frame(cbind(name=varlistDe,desc=varlistDe))
            
            
            varlist_DeAll <- colnames(DEdataframe)
            varlist_DeAll <- varlist_DeAll[!(varlist_DeAll %in% c(outcome))]
            varlist_DeAll <- as.data.frame(cbind(name=varlist_DeAll,desc=varlist_DeAll))
            
  
            
            pDe <- univariate_Wilcoxon(DEdataframeTrain,outcome,pvalue=sig_pvalue);
            topDecorNames <- names(pDe);
  
            dc <- getLatentCoefficients(DEdataframeTrain)
            deNames_in_dc <- topDecorNames[topDecorNames %in% names(dc)]
            selectedlist <- dc[deNames_in_dc]
            theDeFormulas <- selectedlist
            
            
            Average_Latent_Size <- c(Average_Latent_Size,length(unlist(theDeFormulas))/length(theDeFormulas))
            
            topSigDe <- topDecorNames
            
            pDeDe <- pDe[names(pDe) %in% varlistDe[,1]]

            La_Significant <- c(La_Significant,length(topSigDe))
            SigDe <- c(SigDe,length(pDeDe))
  
            
            mlKNN <- KNN_method(formula(paste(outcome,"~.")),DEdataframeTrain[,c(outcome,topSigDe)])
            psb <- predictionStats_binary(cbind(dataframeTest[,outcome],
                                                  predict(mlKNN,
                                                  DEdataframe[,c(outcome,topSigDe)])),plotname,cex=0.6)
            pbKNNauc <- c(pbKNNauc,psb$aucs[1])
            pbKNNacc <- c(pbKNNacc,psb$accc[1])
          }
          totBaM <- rbind(totBaM,totBa)
          totDeM <- rbind(totDeM,totDe)
          toUnmatM <- rbind(toUnmatM,toUnmat)
          unalteredM <- rbind(unalteredM,unaltered)
          Decorrleated_FractionM <- rbind(Decorrleated_FractionM,Decorrleated_Fraction)
          Base_FractionM <- rbind(Base_FractionM,Base_Fraction)
          Unaltered_FractionM <- rbind(Unaltered_FractionM,Unaltered_Fraction)
          sparcityM <- rbind(sparcityM,sparcity)
          Average_Latent_SizeM <- rbind(Average_Latent_SizeM,Average_Latent_Size)
          SigDeM <- rbind(SigDeM,SigDe)
          La_SignificantM <- rbind(La_SignificantM,La_Significant)
          pbKNNaucM <- rbind(pbKNNaucM,pbKNNauc)
          pbKNNaccM <- rbind(pbKNNaccM,pbKNNacc)
  
        }
      }
    }
  }
}

[1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.05 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.2 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.4 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.6 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.8 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.95 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.05 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.2 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.4 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.6 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.8 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.95 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.05 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.2 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.4 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.6 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.8 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.95 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.05 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.2 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.4 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.6 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.8 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.95 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.05 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.2 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.4 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.6 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.8 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.95 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.05 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.2 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.4 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.6 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.8 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.95 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.05 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.2 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.4 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.6 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.8 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.95 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.05 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.2 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.4 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.6 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.8 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.95 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.05 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.2 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.4 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.6 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.8 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.95 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.05 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.2 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.4 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.6 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.8 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.95 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.05 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.2 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.4 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.6 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.8 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.95 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.05 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.2 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.4 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.6 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.8 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.95 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.05 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.2 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.4 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.6 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.8 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.95 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.05 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.2 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.4 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.6 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.8 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.95 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.05 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.2 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.4 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.6 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.8 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.95 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.05 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.2 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.4 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.6 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.8 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.95 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.05 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.2 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.4 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.6 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.8 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.95 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.05 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.2 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.4 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.6 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.8 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.95 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.05 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.2 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.4 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.6 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.8 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.95 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.05 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.2 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.4 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.6 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.8 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.95 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.05 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.2 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.4 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.6 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.8 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.95 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.05 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.2 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.4 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.6 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.8 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.95 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.05 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.2 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.4 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.6 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.8 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.95 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.05 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.2 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.4 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.6 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.8 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.95

par(op)

1.2.1 Printing the analysis outputs

par(op)
par(mfrow=c(1,2),cex=0.6)

rownames(totBaM) <- thenames
rownames(totDeM) <- thenames
rownames(toUnmatM) <- thenames
rownames(unalteredM) <- thenames
rownames(Decorrleated_FractionM) <- thenames
rownames(Base_FractionM) <- thenames
rownames(Unaltered_FractionM) <- thenames
rownames(sparcityM) <- thenames
rownames(Average_Latent_SizeM) <- thenames
rownames(SigDeM) <- thenames
rownames(La_SignificantM) <- thenames
rownames(pbKNNaucM) <- thenames
rownames(pbKNNaccM) <- thenames

colnames(totBaM) <- thr
colnames(totDeM) <- thr
colnames(toUnmatM) <- thr
colnames(unalteredM) <- thr
colnames(Decorrleated_FractionM) <- thr
colnames(Base_FractionM) <- thr
colnames(Unaltered_FractionM) <- thr
colnames(sparcityM) <- thr
colnames(Average_Latent_SizeM) <- thr
colnames(SigDeM) <- thr
colnames(La_SignificantM) <- thr
colnames(pbKNNaucM) <- thr
colnames(pbKNNaccM) <- thr

pander::pander(totFe)

450

pander::pander(totBaM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	72	58	68	127	142	83
T_Blind_fast_LM_TRUE	48	20	27	96	127	83
T_Blind_fast_RLM_FALSE	72	58	68	127	142	83
T_Blind_fast_RLM_TRUE	48	20	27	96	127	83
T_Blind_pearson_LM_FALSE	55	35	71	128	142	83
T_Blind_pearson_LM_TRUE	37	23	23	98	127	83
T_Blind_pearson_RLM_FALSE	89	81	90	129	138	82
T_Blind_pearson_RLM_TRUE	75	72	65	88	124	82
T_Blind_spearman_LM_FALSE	68	69	60	121	137	52
T_Blind_spearman_LM_TRUE	54	55	19	94	119	52
T_Blind_spearman_RLM_FALSE	83	78	72	118	140	50
T_Blind_spearman_RLM_TRUE	73	75	43	94	122	50
T_Driven_fast_LM_FALSE	95	44	61	126	138	83
T_Driven_fast_LM_TRUE	66	60	38	100	127	83
T_Driven_fast_RLM_FALSE	95	44	61	126	138	83
T_Driven_fast_RLM_TRUE	66	60	38	100	127	83
T_Driven_pearson_LM_FALSE	52	61	61	125	138	83
T_Driven_pearson_LM_TRUE	53	48	40	99	127	83
T_Driven_pearson_RLM_FALSE	92	93	95	125	134	80
T_Driven_pearson_RLM_TRUE	83	73	73	103	123	80
T_Driven_spearman_LM_FALSE	67	60	67	119	134	51
T_Driven_spearman_LM_TRUE	66	54	30	96	123	51
T_Driven_spearman_RLM_FALSE	84	68	76	115	134	50
T_Driven_spearman_RLM_TRUE	76	76	65	98	125	50

pander::pander(totDeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	357	377	377	276	178	78
T_Blind_fast_LM_TRUE	380	422	421	306	194	79
T_Blind_fast_RLM_FALSE	357	377	377	276	178	78
T_Blind_fast_RLM_TRUE	380	422	421	306	194	79
T_Blind_pearson_LM_FALSE	381	407	367	276	178	78
T_Blind_pearson_LM_TRUE	390	414	424	305	194	79
T_Blind_pearson_RLM_FALSE	314	324	315	264	175	78
T_Blind_pearson_RLM_TRUE	334	335	339	290	189	79
T_Blind_spearman_LM_FALSE	362	361	383	280	175	52
T_Blind_spearman_LM_TRUE	370	368	428	316	192	52
T_Blind_spearman_RLM_FALSE	331	338	357	273	172	52
T_Blind_spearman_RLM_TRUE	342	341	394	315	190	52
T_Driven_fast_LM_FALSE	327	391	383	282	178	78
T_Driven_fast_LM_TRUE	357	363	410	306	190	79
T_Driven_fast_RLM_FALSE	327	391	383	282	178	78
T_Driven_fast_RLM_TRUE	357	363	410	306	190	79
T_Driven_pearson_LM_FALSE	375	370	384	282	178	78
T_Driven_pearson_LM_TRUE	371	379	408	306	190	79
T_Driven_pearson_RLM_FALSE	306	309	313	266	174	78
T_Driven_pearson_RLM_TRUE	323	329	327	284	186	79
T_Driven_spearman_LM_FALSE	357	365	373	288	174	52
T_Driven_spearman_LM_TRUE	357	369	412	314	184	52
T_Driven_spearman_RLM_FALSE	329	349	348	284	171	52
T_Driven_spearman_RLM_TRUE	342	338	369	308	182	52

pander::pander(toUnmatM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	72	58	68	127	142	83
T_Blind_fast_LM_TRUE	48	20	27	96	127	83
T_Blind_fast_RLM_FALSE	72	58	68	127	142	83
T_Blind_fast_RLM_TRUE	48	20	27	96	127	83
T_Blind_pearson_LM_FALSE	55	35	71	128	142	83
T_Blind_pearson_LM_TRUE	37	23	23	98	127	83
T_Blind_pearson_RLM_FALSE	89	81	90	129	138	82
T_Blind_pearson_RLM_TRUE	75	72	65	88	124	82
T_Blind_spearman_LM_FALSE	68	69	60	121	137	52
T_Blind_spearman_LM_TRUE	54	55	19	94	119	52
T_Blind_spearman_RLM_FALSE	83	78	72	118	140	50
T_Blind_spearman_RLM_TRUE	73	75	43	94	122	50
T_Driven_fast_LM_FALSE	95	44	61	126	138	83
T_Driven_fast_LM_TRUE	66	60	38	100	127	83
T_Driven_fast_RLM_FALSE	95	44	61	126	138	83
T_Driven_fast_RLM_TRUE	66	60	38	100	127	83
T_Driven_pearson_LM_FALSE	52	61	61	125	138	83
T_Driven_pearson_LM_TRUE	53	48	40	99	127	83
T_Driven_pearson_RLM_FALSE	92	93	95	125	134	80
T_Driven_pearson_RLM_TRUE	83	73	73	103	123	80
T_Driven_spearman_LM_FALSE	67	60	67	119	134	51
T_Driven_spearman_LM_TRUE	66	54	30	96	123	51
T_Driven_spearman_RLM_FALSE	84	68	76	115	134	50
T_Driven_spearman_RLM_TRUE	76	76	65	98	125	50

pander::pander(unalteredM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	93	73	73	174	272	372
T_Blind_fast_LM_TRUE	70	28	29	144	256	371
T_Blind_fast_RLM_FALSE	93	73	73	174	272	372
T_Blind_fast_RLM_TRUE	70	28	29	144	256	371
T_Blind_pearson_LM_FALSE	69	43	83	174	272	372
T_Blind_pearson_LM_TRUE	60	36	26	145	256	371
T_Blind_pearson_RLM_FALSE	136	126	135	186	275	372
T_Blind_pearson_RLM_TRUE	116	115	111	160	261	371
T_Blind_spearman_LM_FALSE	88	89	67	170	275	398
T_Blind_spearman_LM_TRUE	80	82	22	134	258	398
T_Blind_spearman_RLM_FALSE	119	112	93	177	278	398
T_Blind_spearman_RLM_TRUE	108	109	56	135	260	398
T_Driven_fast_LM_FALSE	123	59	67	168	272	372
T_Driven_fast_LM_TRUE	93	87	40	144	260	371
T_Driven_fast_RLM_FALSE	123	59	67	168	272	372
T_Driven_fast_RLM_TRUE	93	87	40	144	260	371
T_Driven_pearson_LM_FALSE	75	80	66	168	272	372
T_Driven_pearson_LM_TRUE	79	71	42	144	260	371
T_Driven_pearson_RLM_FALSE	144	141	137	184	276	372
T_Driven_pearson_RLM_TRUE	127	121	123	166	264	371
T_Driven_spearman_LM_FALSE	93	85	77	162	276	398
T_Driven_spearman_LM_TRUE	93	81	38	136	266	398
T_Driven_spearman_RLM_FALSE	121	101	102	166	279	398
T_Driven_spearman_RLM_TRUE	108	112	81	142	268	398

pander::pander(Decorrleated_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.793	0.838	0.838	0.613	0.396	0.173
T_Blind_fast_LM_TRUE	0.844	0.938	0.936	0.680	0.431	0.176
T_Blind_fast_RLM_FALSE	0.793	0.838	0.838	0.613	0.396	0.173
T_Blind_fast_RLM_TRUE	0.844	0.938	0.936	0.680	0.431	0.176
T_Blind_pearson_LM_FALSE	0.847	0.904	0.816	0.613	0.396	0.173
T_Blind_pearson_LM_TRUE	0.867	0.920	0.942	0.678	0.431	0.176
T_Blind_pearson_RLM_FALSE	0.698	0.720	0.700	0.587	0.389	0.173
T_Blind_pearson_RLM_TRUE	0.742	0.744	0.753	0.644	0.420	0.176
T_Blind_spearman_LM_FALSE	0.804	0.802	0.851	0.622	0.389	0.116
T_Blind_spearman_LM_TRUE	0.822	0.818	0.951	0.702	0.427	0.116
T_Blind_spearman_RLM_FALSE	0.736	0.751	0.793	0.607	0.382	0.116
T_Blind_spearman_RLM_TRUE	0.760	0.758	0.876	0.700	0.422	0.116
T_Driven_fast_LM_FALSE	0.727	0.869	0.851	0.627	0.396	0.173
T_Driven_fast_LM_TRUE	0.793	0.807	0.911	0.680	0.422	0.176
T_Driven_fast_RLM_FALSE	0.727	0.869	0.851	0.627	0.396	0.173
T_Driven_fast_RLM_TRUE	0.793	0.807	0.911	0.680	0.422	0.176
T_Driven_pearson_LM_FALSE	0.833	0.822	0.853	0.627	0.396	0.173
T_Driven_pearson_LM_TRUE	0.824	0.842	0.907	0.680	0.422	0.176
T_Driven_pearson_RLM_FALSE	0.680	0.687	0.696	0.591	0.387	0.173
T_Driven_pearson_RLM_TRUE	0.718	0.731	0.727	0.631	0.413	0.176
T_Driven_spearman_LM_FALSE	0.793	0.811	0.829	0.640	0.387	0.116
T_Driven_spearman_LM_TRUE	0.793	0.820	0.916	0.698	0.409	0.116
T_Driven_spearman_RLM_FALSE	0.731	0.776	0.773	0.631	0.380	0.116
T_Driven_spearman_RLM_TRUE	0.760	0.751	0.820	0.684	0.404	0.116

pander::pander(Base_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.1600	0.1289	0.1511	0.282	0.316	0.184
T_Blind_fast_LM_TRUE	0.1067	0.0444	0.0600	0.213	0.282	0.184
T_Blind_fast_RLM_FALSE	0.1600	0.1289	0.1511	0.282	0.316	0.184
T_Blind_fast_RLM_TRUE	0.1067	0.0444	0.0600	0.213	0.282	0.184
T_Blind_pearson_LM_FALSE	0.1222	0.0778	0.1578	0.284	0.316	0.184
T_Blind_pearson_LM_TRUE	0.0822	0.0511	0.0511	0.218	0.282	0.184
T_Blind_pearson_RLM_FALSE	0.1978	0.1800	0.2000	0.287	0.307	0.182
T_Blind_pearson_RLM_TRUE	0.1667	0.1600	0.1444	0.196	0.276	0.182
T_Blind_spearman_LM_FALSE	0.1511	0.1533	0.1333	0.269	0.304	0.116
T_Blind_spearman_LM_TRUE	0.1200	0.1222	0.0422	0.209	0.264	0.116
T_Blind_spearman_RLM_FALSE	0.1844	0.1733	0.1600	0.262	0.311	0.111
T_Blind_spearman_RLM_TRUE	0.1622	0.1667	0.0956	0.209	0.271	0.111
T_Driven_fast_LM_FALSE	0.2111	0.0978	0.1356	0.280	0.307	0.184
T_Driven_fast_LM_TRUE	0.1467	0.1333	0.0844	0.222	0.282	0.184
T_Driven_fast_RLM_FALSE	0.2111	0.0978	0.1356	0.280	0.307	0.184
T_Driven_fast_RLM_TRUE	0.1467	0.1333	0.0844	0.222	0.282	0.184
T_Driven_pearson_LM_FALSE	0.1156	0.1356	0.1356	0.278	0.307	0.184
T_Driven_pearson_LM_TRUE	0.1178	0.1067	0.0889	0.220	0.282	0.184
T_Driven_pearson_RLM_FALSE	0.2044	0.2067	0.2111	0.278	0.298	0.178
T_Driven_pearson_RLM_TRUE	0.1844	0.1622	0.1622	0.229	0.273	0.178
T_Driven_spearman_LM_FALSE	0.1489	0.1333	0.1489	0.264	0.298	0.113
T_Driven_spearman_LM_TRUE	0.1467	0.1200	0.0667	0.213	0.273	0.113
T_Driven_spearman_RLM_FALSE	0.1867	0.1511	0.1689	0.256	0.298	0.111
T_Driven_spearman_RLM_TRUE	0.1689	0.1689	0.1444	0.218	0.278	0.111

pander::pander(Unaltered_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.207	0.1622	0.1622	0.387	0.604	0.827
T_Blind_fast_LM_TRUE	0.156	0.0622	0.0644	0.320	0.569	0.824
T_Blind_fast_RLM_FALSE	0.207	0.1622	0.1622	0.387	0.604	0.827
T_Blind_fast_RLM_TRUE	0.156	0.0622	0.0644	0.320	0.569	0.824
T_Blind_pearson_LM_FALSE	0.153	0.0956	0.1844	0.387	0.604	0.827
T_Blind_pearson_LM_TRUE	0.133	0.0800	0.0578	0.322	0.569	0.824
T_Blind_pearson_RLM_FALSE	0.302	0.2800	0.3000	0.413	0.611	0.827
T_Blind_pearson_RLM_TRUE	0.258	0.2556	0.2467	0.356	0.580	0.824
T_Blind_spearman_LM_FALSE	0.196	0.1978	0.1489	0.378	0.611	0.884
T_Blind_spearman_LM_TRUE	0.178	0.1822	0.0489	0.298	0.573	0.884
T_Blind_spearman_RLM_FALSE	0.264	0.2489	0.2067	0.393	0.618	0.884
T_Blind_spearman_RLM_TRUE	0.240	0.2422	0.1244	0.300	0.578	0.884
T_Driven_fast_LM_FALSE	0.273	0.1311	0.1489	0.373	0.604	0.827
T_Driven_fast_LM_TRUE	0.207	0.1933	0.0889	0.320	0.578	0.824
T_Driven_fast_RLM_FALSE	0.273	0.1311	0.1489	0.373	0.604	0.827
T_Driven_fast_RLM_TRUE	0.207	0.1933	0.0889	0.320	0.578	0.824
T_Driven_pearson_LM_FALSE	0.167	0.1778	0.1467	0.373	0.604	0.827
T_Driven_pearson_LM_TRUE	0.176	0.1578	0.0933	0.320	0.578	0.824
T_Driven_pearson_RLM_FALSE	0.320	0.3133	0.3044	0.409	0.613	0.827
T_Driven_pearson_RLM_TRUE	0.282	0.2689	0.2733	0.369	0.587	0.824
T_Driven_spearman_LM_FALSE	0.207	0.1889	0.1711	0.360	0.613	0.884
T_Driven_spearman_LM_TRUE	0.207	0.1800	0.0844	0.302	0.591	0.884
T_Driven_spearman_RLM_FALSE	0.269	0.2244	0.2267	0.369	0.620	0.884
T_Driven_spearman_RLM_TRUE	0.240	0.2489	0.1800	0.316	0.596	0.884

pander::pander(sparcityM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.00526	0.00586	0.00790	0.00416	0.00330	0.00265
T_Blind_fast_LM_TRUE	0.00595	0.00987	0.01249	0.00457	0.00341	0.00266
T_Blind_fast_RLM_FALSE	0.00526	0.00586	0.00790	0.00416	0.00330	0.00265
T_Blind_fast_RLM_TRUE	0.00595	0.00987	0.01249	0.00457	0.00341	0.00266
T_Blind_pearson_LM_FALSE	0.00575	0.00769	0.00621	0.00413	0.00330	0.00265
T_Blind_pearson_LM_TRUE	0.00577	0.00914	0.01491	0.00462	0.00341	0.00266
T_Blind_pearson_RLM_FALSE	0.00448	0.00475	0.00481	0.00403	0.00331	0.00265
T_Blind_pearson_RLM_TRUE	0.00492	0.00487	0.00506	0.00454	0.00341	0.00266
T_Blind_spearman_LM_FALSE	0.00577	0.00577	0.00765	0.00416	0.00324	0.00249
T_Blind_spearman_LM_TRUE	0.00530	0.00531	0.01131	0.00438	0.00333	0.00249
T_Blind_spearman_RLM_FALSE	0.00492	0.00505	0.00574	0.00420	0.00329	0.00248
T_Blind_spearman_RLM_TRUE	0.00493	0.00495	0.00807	0.00449	0.00337	0.00248
T_Driven_fast_LM_FALSE	0.00472	0.00739	0.00828	0.00426	0.00327	0.00265
T_Driven_fast_LM_TRUE	0.00547	0.00575	0.01221	0.00462	0.00336	0.00266
T_Driven_fast_RLM_FALSE	0.00472	0.00739	0.00828	0.00426	0.00327	0.00265
T_Driven_fast_RLM_TRUE	0.00547	0.00575	0.01221	0.00462	0.00336	0.00266
T_Driven_pearson_LM_FALSE	0.00556	0.00561	0.00826	0.00425	0.00327	0.00265
T_Driven_pearson_LM_TRUE	0.00551	0.00587	0.01137	0.00467	0.00335	0.00266
T_Driven_pearson_RLM_FALSE	0.00444	0.00451	0.00460	0.00407	0.00327	0.00264
T_Driven_pearson_RLM_TRUE	0.00480	0.00481	0.00480	0.00442	0.00336	0.00265
T_Driven_spearman_LM_FALSE	0.00538	0.00539	0.00683	0.00417	0.00321	0.00248
T_Driven_spearman_LM_TRUE	0.00514	0.00539	0.01046	0.00446	0.00328	0.00248
T_Driven_spearman_RLM_FALSE	0.00490	0.00518	0.00550	0.00434	0.00322	0.00248
T_Driven_spearman_RLM_TRUE	0.00509	0.00509	0.00650	0.00438	0.00329	0.00248

pander::pander(Average_Latent_SizeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	3.12	2.63	5.57	2.83	2.25	2.33
T_Blind_fast_LM_TRUE	3.15	5.00	5.20	2.89	2.07	2.33
T_Blind_fast_RLM_FALSE	3.12	2.63	5.57	2.83	2.25	2.33
T_Blind_fast_RLM_TRUE	3.15	5.00	5.20	2.89	2.07	2.33
T_Blind_pearson_LM_FALSE	2.47	3.09	3.00	2.78	2.29	2.40
T_Blind_pearson_LM_TRUE	2.60	5.83	6.44	3.50	2.26	2.40
T_Blind_pearson_RLM_FALSE	2.35	2.52	2.91	2.32	2.35	2.20
T_Blind_pearson_RLM_TRUE	2.71	2.66	2.95	2.35	2.28	2.20
T_Blind_spearman_LM_FALSE	3.40	3.40	4.00	2.63	2.31	2.12
T_Blind_spearman_LM_TRUE	3.00	3.00	5.87	2.57	2.26	2.10
T_Blind_spearman_RLM_FALSE	2.71	2.90	3.12	2.35	2.44	2.00
T_Blind_spearman_RLM_TRUE	2.79	2.58	5.19	2.42	2.33	2.00
T_Driven_fast_LM_FALSE	3.00	2.67	4.00	3.38	2.41	2.38
T_Driven_fast_LM_TRUE	3.08	2.78	8.29	2.80	2.33	2.38
T_Driven_fast_RLM_FALSE	3.00	2.67	4.00	3.38	2.41	2.38
T_Driven_fast_RLM_TRUE	3.08	2.78	8.29	2.80	2.33	2.38
T_Driven_pearson_LM_FALSE	2.65	2.88	4.00	3.33	2.38	2.44
T_Driven_pearson_LM_TRUE	3.00	2.67	8.11	3.00	2.39	2.44
T_Driven_pearson_RLM_FALSE	2.40	2.77	2.92	2.47	2.38	2.33
T_Driven_pearson_RLM_TRUE	2.62	2.52	3.00	2.60	2.21	2.33
T_Driven_spearman_LM_FALSE	3.05	3.00	5.53	2.83	2.28	2.00
T_Driven_spearman_LM_TRUE	2.92	2.89	5.29	2.44	2.26	2.00
T_Driven_spearman_RLM_FALSE	2.73	2.85	2.57	2.76	2.33	2.00
T_Driven_spearman_RLM_TRUE	2.87	3.04	3.29	2.65	2.38	2.00

pander::pander(SigDeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	17	19	7	6	16	9
T_Blind_fast_LM_TRUE	13	7	5	9	14	9
T_Blind_fast_RLM_FALSE	17	19	7	6	16	9
T_Blind_fast_RLM_TRUE	13	7	5	9	14	9
T_Blind_pearson_LM_FALSE	15	11	12	9	17	10
T_Blind_pearson_LM_TRUE	10	6	9	12	19	10
T_Blind_pearson_RLM_FALSE	31	27	22	19	17	5
T_Blind_pearson_RLM_TRUE	24	29	22	20	18	5
T_Blind_spearman_LM_FALSE	20	20	13	19	16	8
T_Blind_spearman_LM_TRUE	13	10	15	14	19	10
T_Blind_spearman_RLM_FALSE	21	20	16	26	18	7
T_Blind_spearman_RLM_TRUE	19	19	21	26	18	8
T_Driven_fast_LM_FALSE	11	6	11	8	17	8
T_Driven_fast_LM_TRUE	12	9	7	10	15	8
T_Driven_fast_RLM_FALSE	11	6	11	8	17	8
T_Driven_fast_RLM_TRUE	12	9	7	10	15	8
T_Driven_pearson_LM_FALSE	26	8	8	9	16	9
T_Driven_pearson_LM_TRUE	9	6	9	11	18	9
T_Driven_pearson_RLM_FALSE	25	22	12	19	13	6
T_Driven_pearson_RLM_TRUE	24	21	15	15	14	6
T_Driven_spearman_LM_FALSE	21	10	15	12	18	9
T_Driven_spearman_LM_TRUE	13	9	21	9	19	10
T_Driven_spearman_RLM_FALSE	22	20	23	25	12	6
T_Driven_spearman_RLM_TRUE	23	23	24	20	16	7

pander::pander(La_SignificantM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	32	31	16	34	81	123
T_Blind_fast_LM_TRUE	26	7	8	33	71	119
T_Blind_fast_RLM_FALSE	32	31	16	34	81	123
T_Blind_fast_RLM_TRUE	26	7	8	33	71	119
T_Blind_pearson_LM_FALSE	27	17	22	38	83	124
T_Blind_pearson_LM_TRUE	19	7	13	41	77	123
T_Blind_pearson_RLM_FALSE	59	45	44	56	85	113
T_Blind_pearson_RLM_TRUE	42	49	42	50	81	113
T_Blind_spearman_LM_FALSE	29	29	24	47	85	133
T_Blind_spearman_LM_TRUE	26	21	18	32	87	137
T_Blind_spearman_RLM_FALSE	33	32	28	64	89	132
T_Blind_spearman_RLM_TRUE	39	39	27	48	87	133
T_Driven_fast_LM_FALSE	31	15	31	53	98	126
T_Driven_fast_LM_TRUE	30	25	24	51	89	125
T_Driven_fast_RLM_FALSE	31	15	31	53	98	126
T_Driven_fast_RLM_TRUE	30	25	24	51	89	125
T_Driven_pearson_LM_FALSE	33	15	27	54	96	127
T_Driven_pearson_LM_TRUE	18	12	26	54	92	126
T_Driven_pearson_RLM_FALSE	53	38	42	70	94	124
T_Driven_pearson_RLM_TRUE	44	36	42	63	91	123
T_Driven_spearman_LM_FALSE	28	12	30	45	99	138
T_Driven_spearman_LM_TRUE	26	12	28	36	98	140
T_Driven_spearman_RLM_FALSE	35	24	41	66	93	133
T_Driven_spearman_RLM_TRUE	42	36	39	53	96	134

pander::pander(pbKNNaucM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.947	0.951	0.928	0.910	0.928	0.936
T_Blind_fast_LM_TRUE	0.921	0.884	0.860	0.885	0.926	0.934
T_Blind_fast_RLM_FALSE	0.947	0.951	0.928	0.910	0.928	0.936
T_Blind_fast_RLM_TRUE	0.921	0.884	0.860	0.885	0.926	0.934
T_Blind_pearson_LM_FALSE	0.876	0.872	0.906	0.910	0.943	0.932
T_Blind_pearson_LM_TRUE	0.840	0.907	0.828	0.905	0.935	0.934
T_Blind_pearson_RLM_FALSE	0.911	0.919	0.925	0.942	0.919	0.954
T_Blind_pearson_RLM_TRUE	0.947	0.954	0.917	0.906	0.884	0.949
T_Blind_spearman_LM_FALSE	0.960	0.960	0.899	0.928	0.946	0.934
T_Blind_spearman_LM_TRUE	0.960	0.939	0.930	0.931	0.943	0.931
T_Blind_spearman_RLM_FALSE	0.913	0.919	0.926	0.921	0.934	0.929
T_Blind_spearman_RLM_TRUE	0.937	0.951	0.837	0.912	0.933	0.921
T_Driven_fast_LM_FALSE	0.945	0.918	0.969	0.931	0.939	0.943
T_Driven_fast_LM_TRUE	0.931	0.909	0.914	0.931	0.945	0.944
T_Driven_fast_RLM_FALSE	0.945	0.918	0.969	0.931	0.939	0.943
T_Driven_fast_RLM_TRUE	0.931	0.909	0.914	0.931	0.945	0.944
T_Driven_pearson_LM_FALSE	0.934	0.900	0.949	0.939	0.939	0.942
T_Driven_pearson_LM_TRUE	0.840	0.889	0.933	0.934	0.932	0.944
T_Driven_pearson_RLM_FALSE	0.876	0.888	0.926	0.936	0.926	0.947
T_Driven_pearson_RLM_TRUE	0.950	0.924	0.947	0.930	0.932	0.947
T_Driven_spearman_LM_FALSE	0.934	0.932	0.910	0.957	0.941	0.939
T_Driven_spearman_LM_TRUE	0.967	0.910	0.938	0.929	0.941	0.939
T_Driven_spearman_RLM_FALSE	0.925	0.920	0.943	0.956	0.916	0.927
T_Driven_spearman_RLM_TRUE	0.933	0.948	0.919	0.955	0.931	0.941

pander::pander(pbKNNaccM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.77	0.736	0.805	0.701	0.724	0.724
T_Blind_fast_LM_TRUE	0.759	0.736	0.77	0.678	0.701	0.736
T_Blind_fast_RLM_FALSE	0.77	0.736	0.805	0.701	0.724	0.724
T_Blind_fast_RLM_TRUE	0.759	0.736	0.77	0.678	0.701	0.736
T_Blind_pearson_LM_FALSE	0.759	0.713	0.701	0.736	0.747	0.724
T_Blind_pearson_LM_TRUE	0.713	0.839	0.724	0.747	0.713	0.724
T_Blind_pearson_RLM_FALSE	0.759	0.747	0.77	0.736	0.713	0.724
T_Blind_pearson_RLM_TRUE	0.759	0.793	0.736	0.736	0.678	0.736
T_Blind_spearman_LM_FALSE	0.77	0.77	0.724	0.793	0.759	0.713
T_Blind_spearman_LM_TRUE	0.782	0.747	0.77	0.759	0.736	0.713
T_Blind_spearman_RLM_FALSE	0.747	0.759	0.77	0.793	0.724	0.724
T_Blind_spearman_RLM_TRUE	0.701	0.678	0.724	0.69	0.713	0.724
T_Driven_fast_LM_FALSE	0.759	0.816	0.793	0.77	0.747	0.724
T_Driven_fast_LM_TRUE	0.793	0.782	0.77	0.713	0.747	0.724
T_Driven_fast_RLM_FALSE	0.759	0.816	0.793	0.77	0.747	0.724
T_Driven_fast_RLM_TRUE	0.793	0.782	0.77	0.713	0.747	0.724
T_Driven_pearson_LM_FALSE	0.793	0.759	0.793	0.759	0.724	0.713
T_Driven_pearson_LM_TRUE	0.69	0.759	0.805	0.759	0.747	0.736
T_Driven_pearson_RLM_FALSE	0.713	0.701	0.77	0.724	0.77	0.713
T_Driven_pearson_RLM_TRUE	0.747	0.77	0.747	0.77	0.759	0.713
T_Driven_spearman_LM_FALSE	0.713	0.828	0.759	0.793	0.747	0.724
T_Driven_spearman_LM_TRUE	0.747	0.793	0.713	0.759	0.747	0.69
T_Driven_spearman_RLM_FALSE	0.736	0.759	0.724	0.77	0.736	0.713
T_Driven_spearman_RLM_TRUE	0.77	0.736	0.69	0.782	0.736	0.713

miny = 0.15
maxy = max(pbKNNaucM)

plot(thr,pbKNNaucM[1,],ylim=c(miny,maxy),
     main="KNN's ROCAUC",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="ROC AUC",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
  lines(thr,pbKNNaucM[ind,],col=ind,lwd=2,lty=ind)
}

legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)

fastRows <- str_detect(rownames(pbKNNaucM),"fast")
pearsonRows <- str_detect(rownames(pbKNNaucM),"pearson")
spearmanRows <- str_detect(rownames(pbKNNaucM),"spearman")
T_BlindRows <- str_detect(rownames(pbKNNaucM),"T_Blind")
corRankRows <- str_detect(rownames(pbKNNaucM),"TRUE")
maxCorRankRows <- str_detect(rownames(pbKNNaucM),"FALSE")
RLMfitMethod <- str_detect(rownames(pbKNNaucM),"RLM")


meanAuc <-  colMeans(pbKNNaucM[fastRows,])
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[pearsonRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[spearmanRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[!T_BlindRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[T_BlindRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[corRankRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[maxCorRankRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[RLMfitMethod,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[!RLMfitMethod,]))
legnames <- c("fast","Pearson","Spearman","T_Driven","T_Blind","SumCor","MaxCor","RLM","LM")


pbKNNaccM <- as.data.frame(pbKNNaccM)
pbKNNaccM[,1:ncol(pbKNNaccM)] <- sapply(pbKNNaccM,as.numeric)

Average_Latent_SizeM <- as.data.frame(Average_Latent_SizeM)
Average_Latent_SizeM[,1:ncol(Average_Latent_SizeM)] <- sapply(Average_Latent_SizeM,as.numeric)
Average_Latent_SizeM[is.na(Average_Latent_SizeM)] <- 0

SigDeM <- as.data.frame(SigDeM)
SigDeM[,1:ncol(SigDeM)] <- sapply(SigDeM,as.numeric)

sparcityM <- as.data.frame(sparcityM)
sparcityM[,1:ncol(sparcityM)] <- sapply(sparcityM,as.numeric)

miny = 0.65
maxy = max(meanAuc)+0.025

plot(thr,meanAuc[1,],ylim=c(miny,maxy),
     main="Mean KNN's ROCAUC",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="ROC AUC",
     type="l",
     col=1,
     lwd=2,
     lty=1)
for (ind in 2:nrow(meanAuc))
{
  lines(thr,meanAuc[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 0.35
maxy = max(pbKNNaccM) + 0.1

plot(thr,pbKNNaccM[1,],ylim=c(miny,maxy),
     main="KNN's Accuracy",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Accuracy",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
  lines(thr,pbKNNaccM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)



meanAcc <-  colMeans(pbKNNaccM[fastRows,])
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[pearsonRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[spearmanRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[!T_BlindRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[T_BlindRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[corRankRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[maxCorRankRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[RLMfitMethod,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[!RLMfitMethod,]))

miny = min(meanAcc)-0.01
maxy = max(meanAcc)+0.025

plot(thr,meanAcc[1,],ylim=c(miny,maxy),
     main="Mean KNN's Accuracy",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Accuracy",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(meanAcc))
{
  lines(thr,meanAcc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 1
maxy = 20*max(Average_Latent_SizeM)

plot(thr,Average_Latent_SizeM[1,],ylim=c(miny,maxy),
     main="Average Size of Latent-Variable",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Size",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(Average_Latent_SizeM))
{
  lines(thr,Average_Latent_SizeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(Average_Latent_SizeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanAccAvgSize <-  colMeans(Average_Latent_SizeM[fastRows,])
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[pearsonRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[spearmanRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!T_BlindRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[T_BlindRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[corRankRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[maxCorRankRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[RLMfitMethod,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!RLMfitMethod,]))

miny =1
maxy = 5*max(meanAccAvgSize)

plot(thr,meanAccAvgSize[1,],ylim=c(miny,maxy),
     main="Mean Size of Average-Latent-Variable",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Size",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanAccAvgSize))
{
  lines(thr,meanAccAvgSize[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(La_SignificantM)
maxy = 20*max(La_SignificantM)

plot(thr,La_SignificantM[1,],ylim=c(miny,maxy),
     main="Number of Discovered Features",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Number of Features",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(La_SignificantM))
{
  lines(thr,La_SignificantM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", rownames(La_SignificantM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanDiscovered <-  colMeans(La_SignificantM[fastRows,])
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[pearsonRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[spearmanRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[!T_BlindRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[T_BlindRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[corRankRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[maxCorRankRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[RLMfitMethod,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[!RLMfitMethod,]))

miny = min(meanDiscovered)
maxy = max(meanDiscovered) + 10

plot(thr,meanDiscovered[1,],ylim=c(miny,maxy),
     main="Average Number of Discovered Features",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Number of Features",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanDiscovered))
{
  lines(thr,meanDiscovered[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

SigDeM[is.na(SigDeM)] <- 0
miny = 1
maxy = 20*max(SigDeM)

plot(thr,SigDeM[1,],ylim=c(miny,maxy),
     main="Number of Significant Latent Variables",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="How Many",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(SigDeM))
{
  lines(thr,SigDeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(SigDeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)



SigLatent <-  colMeans(SigDeM[fastRows,])
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[pearsonRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[spearmanRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[!T_BlindRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[T_BlindRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[corRankRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[maxCorRankRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[RLMfitMethod,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[!RLMfitMethod,]))

miny = 1
maxy = max(SigLatent) + 10


plot(thr,SigLatent[1,],ylim=c(miny,maxy),
     main="Average # of Significant Latent Variables",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="How Many",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(SigLatent))
{
  lines(thr,SigLatent[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

sparcityM[is.na(sparcityM)] <- 0
miny = min(sparcityM)
maxy = max(sparcityM) + 0.75 

plot(thr,sparcityM[1,],ylim=c(miny,maxy),
     main="Matrix Sparcity",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Sparcity",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(sparcityM))
{
  lines(thr,sparcityM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(sparcityM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanSparcity <-  colMeans(sparcityM[fastRows,])
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[pearsonRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[spearmanRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[!T_BlindRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[T_BlindRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[corRankRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[maxCorRankRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[RLMfitMethod,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[!RLMfitMethod,]))

miny = min(meanSparcity)
maxy = max(meanSparcity)+0.25


plot(thr,meanSparcity[1,],ylim=c(miny,maxy),
     main="Mean Matrix Sparcity",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Sparcity",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanSparcity))
{
  lines(thr,meanSparcity[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

1.2.2 Saving All

save.image("~/GitHub/LatentBiomarkers/RMD/DARWIN_ALL_Options.RData")